Commonly used dimensionality reduction methods in biology include principal component analysis (PCA), multidimensional scaling (MDS), and t-distributed stochastic neighbor embedding (t-SNE) [1]}. These methods provide valuable insights across a variety of applications (see ref. [2]} for a comparison of embedding methods with high-throughput sequencing data). Here we present examples of their limitations in the context of metabolic trait space reconstruction. Consistent with prior results [2]}, we observe that PCA identifies some important global contrasts but misses fine details, that MDS is highly sensitive to outliers, and that neighbor embedding methods like t-SNE find some localized [4]} clusters by preserving local data structures but cannot reliably identify global structures.
